Although standard mixed effects models are useful in a range of
studies, other approaches must often be used in correlation with
them when studying complex or incomplete data. Mixed Effects Models
for Complex Data discusses commonly used mixed effects models and
presents appropriate approaches to address dropouts, missing data,
measurement errors, censoring, and outliers. For each class of
mixed effects model, the author reviews the corresponding class of
regression model for cross-sectional data. An overview of general
models and methods, along with motivating examples After presenting
real data examples and outlining general approaches to the analysis
of longitudinal/clustered data and incomplete data, the book
introduces linear mixed effects (LME) models, generalized linear
mixed models (GLMMs), nonlinear mixed effects (NLME) models, and
semiparametric and nonparametric mixed effects models. It also
includes general approaches for the analysis of complex data with
missing values, measurement errors, censoring, and outliers.
Self-contained coverage of specific topicsSubsequent chapters delve
more deeply into missing data problems, covariate measurement
errors, and censored responses in mixed effects models. Focusing on
incomplete data, the book also covers survival and frailty models,
joint models of survival and longitudinal data, robust methods for
mixed effects models, marginal generalized estimating equation
(GEE) models for longitudinal or clustered data, and Bayesian
methods for mixed effects models. Background materialIn the
appendix, the author provides background information, such as
likelihood theory, the Gibbs sampler, rejection and importance
sampling methods, numerical integration methods, optimization
methods, bootstrap, and matrix algebra. Failure to properly address
missing data, measurement errors, and other issues in statistical
analyses can lead
General
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